Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Epigenetic phase variation in the gut microbiome enhances bacterial adaptation.

Cell host & microbe·2026

Same author

Prediction of Postoperative Pulmonary Complications in Geriatric Patients with Hip Fracture Using Lung Ultrasonography Score and Diaphragmatic Mobility.

Clinical interventions in aging·2026

Same author

Genetic dissection and validation of major and stable QTLs for the flag leaf size-related traits in common wheat (Triticum aestivum L.).

BMC plant biology·2026

Same author

Cardiometabolic and Renal Outcomes in Semaglutide Users with Type 2 Diabetes Achieving Glycemic and Weight Goals: An Observational Cohort Study.

Advances in therapy·2026

Same author

A clinically useful nomogram integrating bedside lung ultrasound and clinical parameters for pulmonary complications after non-thoracic surgery in blunt chest trauma patients.

Frontiers in medicine·2026

Same author

Salt form and phosphate modification of an SN-38-nitrogen mustard conjugate: Overcoming water solubility limitations, combined efficacy, and broadened antitumor Spectrum.

Bioorganic chemistry·2026

Search research articles

Related Experiment Video

Updated: Jun 27, 2025

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

Deep learning-aided 3D proxy-bridged region-growing framework for multi-organ segmentation.

Zhihong Chen^1,2, Lisha Yao^2,3, Yue Liu^1,4

¹Institute of Computing Science and Technology, Guangzhou University, Guangzhou, 510006, China.

Scientific Reports

|April 29, 2024

Summary

This study introduces a 3D proxy-bridged region-growing framework for liver and spleen segmentation in CT images. The method achieves high accuracy with reduced annotation needs and lower GPU resource demands compared to deep learning approaches.

Keywords:

3D CT image Deep learning Multi-organ segmentation Proxy-bridging Region-growing

More Related Videos

Author Spotlight: Bridging Gaps in Anatomy and Establishing a Foundation for Algorithmic Studies

Author Spotlight: Bridging Gaps in Anatomy and Establishing a Foundation for Algorithmic Studies

Published on: December 15, 2023

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

Related Experiment Videos

Last Updated: Jun 27, 2025

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

Author Spotlight: Bridging Gaps in Anatomy and Establishing a Foundation for Algorithmic Studies

Author Spotlight: Bridging Gaps in Anatomy and Establishing a Foundation for Algorithmic Studies

Published on: December 15, 2023

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

Area of Science:

Medical Imaging
Computer-Aided Diagnosis
Radiotherapy Planning

Background:

Accurate 3D multi-organ segmentation in CT images is crucial for medical applications.
Current deep learning methods require extensive manual annotations and high GPU resources.
Challenges exist in efficient and resource-light 3D segmentation.

Purpose of the Study:

To develop a novel 3D framework for liver and spleen segmentation.
To reduce the reliance on manual annotations and high computational costs.
To improve the efficiency of computer-aided diagnosis and radiotherapy planning.

Main Methods:

A 3D proxy-bridged region-growing framework is proposed.
Key slices are identified using intensity histograms for seed point calculation.
Segmentation is performed on superpixel images to mitigate noise, extended iteratively across slices.

Main Results:

The framework achieved an average Dice Similarity Coefficient of ~0.93 for liver and spleen segmentation.
A Jaccard Similarity Coefficient of ~0.88 was obtained.
The method demonstrated comparable performance to deep learning models with reduced annotation and GPU requirements.

Conclusions:

The proposed framework offers an efficient alternative for 3D liver and spleen segmentation.
It significantly lowers the demand for manual annotations and GPU resources.
This approach holds promise for enhancing computer-aided diagnosis and radiotherapy planning.